Pneumatic tires used for motor vehicles are tested for peripheral irregularity of the tire tread region of the tire, known as “runout”. Runout can take two basic forms, based chiefly on the character of the peripheral irregularity: 1) “non-local” runout caused by gross irregularities, which occur over large tread regions out of round with the rest of the tire tread and may, if significant enough, produce relatively low frequency ride vibrations (e.g., between 5 and 30 Hz), and the like; and 2) “local” runout caused by sharp irregularities, which are observed at tread regions that are locally elevated or depressed with respect to the adjacent tire tread. If significant enough, these sharp irregularities produce periodically modulated cabin interior sound (e.g., above 100 Hz) during vehicle movement, usually on a smooth road at highway speeds (e.g., above about 40 MPH), and is audibly detectable by passengers. This sound is commonly perceived as a thumping sound and is referred to as “helicopter noise” due to its perceived similarity to the sound of a distant helicopter.
Each tire tested is either accepted (passed) or not accepted (not passed) based upon the detection of the runout and predetermined criteria for the acceptance or non-acceptance of the tire with respect to predetermined characteristics of the detected runout.
Referring now to FIGS. 1 and 1A, the prior art tire runout detection apparatus 10 is schematically depicted. A tire 12 is placed onto a spindle 14 at which the tire spins about an axis A, wherein the spinning is induced by a motor 14a connected to the shaft of the spindle. A loading drum 16 is firmly pressed against the tread 12a of the tire 12, wherein the loading drum 16 may or may not be equipped with one or more force sensors (not shown) for sensing nonuniform tire variations. At least one runout detector 20 is disposed adjacent the tire tread 12a to detect either or both local and nonlocal runout of the tire. At least one runout detector provides a signal which is provided to an electronic circuit 18. The runout detector 20 may take a variety of forms: either contact types, such as for non-limiting example leaf contacts, potentiometers, and linear variable differential transformers (LVDTs); or non-contact types, such as for non-limiting example capacitive, optical (particularly laser displacement sensors and laser “fences”), etc.
The “raw” runout signal 22a typically produced by the at least one runout detector is represented at FIG. 1B, which is a graph 22 of raw runout (in inches) in relation to a normalized tire tread location of the tire versus circumferential (i.e., peripheral) location as a function of angular degrees of rotation of one tire revolution.
One technique used in the art to detect an objectionable runout is to measure peak-to-peak amplitude of the raw runout signal over a series of an angular degree range of tire rotation, the series completing a full tire rotation (“window peak-to-peak” method). Another technique used in the prior art is to measure non-local runout either using at least one runout detector 20 or a force sensor at the loading drum; however, this technique does not address local runout which can produce “helicopter noise”.
Tire runout variation is rich in low tire order content, e.g., 1st to 5th tire orders, due to such imperfections as eccentricity and other long wavelength distortions arising from pre-cured natural manufacturing variations interacting with the relatively compliant pre-vulcanized green tire structure. This pre-vulcanized green tire structure, furthermore, will naturally react to even sharp irregularities with a distributed deformation causing adjacent surface area to likewise exhibit a deformation from the pre-distorted shape. This non-local runout comprises lower tire order contributions to the runout, and does not produce “helicopter noise”.
With regard to the term “tire order”, the “1st tire order” content is the term of a Fourier sine series representation of the periodic tire runout signal, whereby the wavelength of the recurrent full sinewave pattern is a complete revolution of the tire; higher tire order content exists with recurrent sinewave patterns over shorter wavelengths such that the 2nd tire order occurs over ½ revolution, the 3rd over ⅓ revolution, etc. Decomposition of the periodic waveforms into Fourier series representations is commonly practiced and is well known to those skilled in the art of periodic waveform analysis.
The challenge is devising a method which provides detection of higher tire order content, the local runout caused by sharp irregularities, in a raw runout signal which is “contaminated” by other lower tire order and non-contributory content to the local runout.
Turning attention now to FIGS. 2A and 2B, examples of the inability of the prior art runout detection techniques to detect objectionable “helicopter noise” are exemplified. At FIG. 2A, a first example of a simulated raw runout signal, plot 24, is depicted in which local runout due to a local sharp irregularity produces a narrow and intense, relatively high tire order signal 24a which is in leading disposition relative to a lower 2nd tire order signal 24b comprising the raw runout signal 24c. At FIG. 2B, a second example of a simulated raw runout signal, plot 26, is depicted in which local runout also due to a sharp irregularity produces a narrow and intense high tire order signal 26a which is in following disposition relative to a lower 2nd tire order signal 26b comprising the raw runout signal 26c. 
At FIG. 2A, the prior art technique for detecting runout of the local type using the window peak-to-peak method of runout detection may falsely identify a tire as unacceptable (a false positive), as for example if runout over 0.03 inches was the threshold, even if the local runout causing the detection, i.e., the high tire order signal 24a, possessed a geometry that would not produce “helicopter noise”. At FIG. 2B, the prior art technique for detecting runout of the local type using the window peak-to-peak method of runout detection may falsely identify a tire as acceptable (a false negative), as for example if runout over 0.03 inches was the threshold, even if the local runout that was not detected, i.e., the high tire order signal 26a, would produce “helicopter noise”.
Limitations of the prior art technique for detecting local runout using the window peak-to-peak method of runout detection where the local runout would produce “helicopter noise” are additionally illustrated as examples in FIG. 2D. FIG. 2D relates to runout of the type indicated at FIGS. 2A and 2B wherein, in FIG. 2D, the horizontal axis depicts the actual peak-to-peak amplitudes, or the true peak-to-peak amplitudes, of the underlying tire tread sharp irregularities causing the local runout and the vertical axis depicts peak-to-peak amplitudes of the underlying tire tread irregularities causing the local runout as measured or calculated for an ideal local runout detection, for local runout detection using the prior art techniques, or (as will be described hereinbelow) using the method according to the present invention.
Plot 34 of FIG. 2D displays results of the analysis of the runout detection applied on idealized simulated runouts of the type indicated in FIGS. 2A and 2B wherein various amplitudes of signal 24a and 26a were superimposed on the fixed amplitude 2nd tire order signals 24b or 26b comprising simulated raw runout signals 24c and 26c, respectively.
Plot 30 of FIG. 2D represents runout detection using the prior art technique applied on a simulated runout of the type indicated in FIG. 2A wherein various amplitudes of signal 24a were superimposed on the fixed amplitude 2nd tire order signal 24b comprising simulated raw runout signal 24c. 
Plot 32 of FIG. 2D represents runout detection using the prior art technique applied on a simulated runout of the type indicated in FIG. 2B wherein various amplitudes of signal 26a were superimposed on the fixed amplitude 2nd tire order signal 26b comprising simulated raw runout signal 26c. 
Plot 36 pertains to the method of the present invention, which will be described later.
In FIG. 2D, a desirable detection should show a direct proportional response to that of the actual or true peak-to-peak amplitudes, i.e., a line that passes through the origin as exemplified by the ideal runout detection plot 34. A displaced line not passing through the origin, on the other hand, such as plot 30 or plot 32 implies tires having local runout that would produce a “helicopter noise” cannot be detected below a threshold level in the presence of contaminating content of the raw runout signal, and cannot detect proper magnitudes for tires with different characters of lower tire order content. As an example, for actual runout of 0.015″, the prior art approach can provide a measured local runout anywhere between 0.015″ to 0.023″, depending on the presence of the lower tire order content in the raw runout signal. This undesirable ambiguous range, i.e., 0.015″ to 0.023″ in this example, of inferred local runout can also degrade further depending on the relative content of the local versus the lower tire order content of the runout.
Thus, the prior art window peak-to-peak runout detection method is limited by false positives (which results in unnecessary non-acceptance of otherwise acceptable tires) and false negatives (which results in acceptance of faulty tires) as well as limited detection capabilities of local runout that produce “helicopter noise”.
Accordingly, what is needed in the art is some methodology which provides detection of the higher tire order content, the local runout caused by sharp irregularities, in a raw runout signal which is “contaminated” by other lower tire order and non-contributory content so that the detection is free of false positive and false negative determinations with respect to the generation of “helicopter noise” by the runout.